In recent years, with the significant increase of various security risk events, the supply chain of a large number of enterprises has been impacted or even interrupted. Supply chain resilience has become the common focus of academy, industry and government. From the perspective of single and multiple configurations, this paper constructs research models for the influencing factors of supply chain resilience. Based on 622 valid samples of enterprises, this study applies structural equation model (SEM) and fuzzy set qualitative comparative analysis (fsQCA) for empirical analysis. The research shows that: 1) Flexibility, agility, reconfiguration, visibility and supply chain cooperation can have positive impacts on supply chain resilience. 2) High flexibility, high agility, high reconfiguration, high visibility and strong cooperative relationship cannot be the necessary conditions for high supply chain resilience alone. 3) There are three driving configurations for high supply chain resilience, namely, high agility configuration consisting of agility * reconfiguration * supply chain cooperation, high visibility configuration consisting of ~flexibility * reconfiguration * visibility * supply chain cooperation, and agile visual dual configuration consisting of ~flexibility * agility * visibility * supply chain cooperation and flexibility * agility * visibility * reconfiguration. The high supply chain resilience is the synergy of multiple factors, and the effective combination of various factors promotes the supply chain resilience in the way of "different paths lead to the same goal". This research systematically discusses the influencing factors of supply chain resilience, which can provide theoretical guidance and managerial insight for enterprises to improve supply chain resilience.

Large models, exemplified by ChatGPT, represent a significant breakthrough in general generative artificial intelligence technology. Their far-reaching implications extend into diverse facets of human production, lifestyle, and cognitive processes, prompting a transformative paradigm shift in the realm of economic research. Originating from the convergence of big data and artificial intelligence, these large models introduce a novel approach to systemic analysis, particularly adept at scrutinizing intricate human economic and social systems. We first discuss the fundamental characteristics and development paradigms of ChatGPT and large models, focusing on how these models effectively tackle the methodological challenges posed by the "curse of dimensionality". We then delve into how ChatGPT and large models will influence the paradigm of economic research. This includes a shift from the assumption of the rational economic man to an AI-driven "human-machine hybrid" economic agent, from the isolated economic individual to the socio-economic individual whose behaviors are measurable, from the separation of macroeconomics and microeconomics to their integration, from the separation of qualitative and quantitative analysis to their unification, and from the long-dominant "small-model" paradigm to a "large-model" paradigm in economic research. We also cover the increasing significance of computer algorithms as a prominent research paradigm and method in economics. Finally, we point out the limitations inherent in artificial intelligence technologies, including large models, when employed as a research method in economics and the broader social sciences.

Option pricing with discrete dividend payments is still a challenge. This paper proposes a novel model by taking the dividends into consideration, and establishes the option price theorem for obtaining the option price. Theoretical analysis shows that the proposed new model can fully take the impact of dividend payments on option price such as the dividend paying time, amount and number, and hence it can produce an accurate price for option. We also conduct a theoretical comparison of the pricing between the newly-proposed model and classic/benchmark, with which the relation and pricing differences between the new model and these models are deeply detected. The numerical results also show that the proposed model can produce highly accurate prices for options and has strong pricing robustness. Based on this, our model can be an excellent alternative of pricing European options written on the underlying asset paying discrete dividends.

Currently, the application of alternative data provides a new perspective for scholars and practitioners in the field of financial investment. This paper builds an analysis platform based on the FarmPredict (factor-augmented regularized model for prediction) framework and deep neural network model, realizing the task of learning trading signals from alternative data such as financial short videos and financial news thereby constructing trading strategies for the China share market. Firstly, match the captured financial news with their corresponding stock code and decompose it into text data and image data. Secondly, the text data is input into the FarmPredict learning framework. We construct and screen the text bag of words by which the phrases are decomposed into common factors and specific factors, and then calculate the score of the news text by the factor regression; We then input the image data into the image recognition deep neural network Google Inception v3 model framework built by the transfer learning technique, thereby outputting the probability that the image represents positive/negative emotions and the image sentiment index and image score. For the captured financial short video, it contains two steps. The first step is to strip the audio data and convert it to audio text data, and use the trained FarmPredict framework to calculate the text score of the short videos; the second step is to extract the key frames of the video, and use the trained image model to calculate the video image score; the text score is summed up with the image score to get the short video data score. Finally, the financial short video score, the text score and the image score of the news report are summed to obtain the stock investment signal, which is used as the basis for constructing the China share stock portfolio and formulating an appropriate investment strategy. Finally, the financial short video score, the text score and the image score of the news report are summed to obtain the stock investment signal, which is used as the basis for constructing the China share stock portfolio and formulating an appropriate investment strategy. The research results show that financial videos and financial news data contain information related to stock prices, which can effectively predict market changes and bring excess returns to investors. The empirical study confirms the importance of alternative data in the Chinese market. By comprehensively analyzing alternative data, this paper provides investors with a comprehensive and effective trading signal extraction method, which can help optimize investment strategies and achieve higher real returns.

Commodity is an important part of industrial production and financial investment, and accurate commodity price forecasting is of great significance to safeguard industrial production and help investors avoid risks. However, most of the existing commodity price forecasting models are point-value models based on closing prices, which ignores the volatility information. Therefore we propose a heteroskedasticity threshold autoregressive interval model with exogenous variables (HTARIX) and apply it to the commodity markets. We also construct a test statistic based on interval-valued data to test whether there is conditional heteroskedasticity in the model, and propose a generalized minimum $D_K$ distance estimation. The advantage of our model is that it can capture the conditional heteroskedasticity and nonlinear features of interval-valued time series models. Compared with the point-valued models, our method contains more information of the data. The empirical results imply that HTARIX model performs better than other comparative models in interval-valued commodity price forecasting.

At present, chat generative pre-trained transformer (ChatGPT) as a representative of the rapid development of large language models, is widely used in stock market investment, algorithmic trading, risk management and other fields. This provides financial investors with new decision-making tools and investment paths. In this paper, we construct an investment trading model based on the bidirectional encoder representation from transformers (BERT) model and chat generative pre-trained transformer (ChatGPT) for the Chinese stock market, and realize the trading signals from financial news text data and traditional financial data. For the text data, the daily financial news is captured and matched with the corresponding stock codes. Secondly, we input the news text data into the trained fine-tuning BERT (FTBERT) model to get the sentiment tendency of each news item, and select the positive financial news as the positive investment trading signals. For the traditional financial data, we use the advanced parsing capability of chat generative pre-trained transformer (ChatGPT) to analyze the historical data of Chinese stock market. By adjusting the prompt to read the data, the key factors for stock investment are constructed, and the daily scores of each stock are output. Finally, the daily investment signals of each stock are obtained based on different data types, which are used as the basis for constructing investment portfolios and building effective investment strategies. The empirical results show that chat generative pre-trained transformer (ChatGPT) effectively determine the sentiment tendency of text. The fine-tuned model can effectively assist quantitative investment and bring investors excessive returns. This study attempts to apply big language modeling to financial investment and shows its potential value in generating stock investment signals. With the continuous development of technology and changes in the market environment, this artificial intelligence-based investment strategy will continue to evolve and create more value for investors.

It is with great pleasure and admiration that we celebrate the 60th birthday of Professor Lihua Xie, a distinguished researcher and visionary leader in the field of robust control and estimation. Prof. Xie’s remarkable journey, marked by outstanding achievements and groundbreaking contributions, has left an indelible mark on the world of engineering and academia.
Prof. Xie’s academic odyssey began at Nanjing University of Science and Technology, where he earned his bachelor’s and master’s degrees in 1983 and 1986, respectively. His pursuit of knowledge led him to the University of Newcastle, Australia, where he obtained his PhD in 1992. Since 1992, he has been a cornerstone of Nanyang Technological University (NTU), Singapore, currently serving as a distinguished professor in the School of Electrical and Electronic Engineering and as the Director of the Centre for Advanced Robotics Technology Innovation (CARTIN), NTU.
One of Prof. Xie’s pivotal contributions lies in the realm of robust control and estimation. His early work in the early 1990s addressed robust solutions for systems with parametric uncertainties, providing a profound understanding of how uncertainty influences control system performance. His pioneering research not only illuminated the impact of uncertainty but also offered effective strategies, particularly for parametric uncertainty, ensuring the robustness of control systems. Prof. Xie was among the first to develop robust estimation techniques for systems grappling with parametric uncertainties, influencing researchers globally since the 1990s.
In the past two decades, Prof. Xie, alongside his co-author, established a groundbreaking equivalence between quantized feedback and robust control. This breakthrough extended the applicability of existing robust control theory to the analysis and design of control systems operating under quantized feedback. His work also unraveled the intricate interplay among data rate, network topology, and agent dynamics in multi-agent consensus - a fundamental challenge in cooperative control. Prof. Xie’s research provided answers to crucial questions, such as determining the minimal data rate and network topology for multi-agent consensus, along with corresponding coding and decoding schemes.
The spectrum of Prof. Xie’s impact extends to compressive sensing, where he and his student established a phase transition relationship between sparsity and recoverability for complex signals. Their continuous compressive sensing algorithms and Vandermonde decomposition theory for multi-level Toeplitz matrices have found applications in array signal processing, marking another significant milestone in his illustrious career.
Beyond theoretical endeavors, Prof. Xie’s practical innovations have revolutionized localization and unmanned systems. His research group’s developments include a WiFi-based indoor positioning system, multi-modality sensor fusion technology, and a fully integrated navigation solution for UAVs. These innovations have found applications in diverse fields, from structure inspection and delivery using UAVs to a low-cost universal navigation system for AGVs in logistics and manufacturing.
In the realm of research and development leadership, Prof. Xie’s impact is equally profound. He is the founding Director of the Delta-NTU Corporate Laboratory for Cyber-physical Systems, which focuses on the development of smart manufacturing and smart learning technologies for industry. Additionally, Prof. Xie established the Centre for Advanced Robotics Technology Innovation, where he currently serves as the Director. The center’s mission is to pioneer advanced sensing and perception technologies, as well as collaborative robotics technologies, with applications in logistics, manufacturing, and elderly care.
As an accomplished researcher, Prof. Xie has demonstrated unparalleled dedication to serving the research community. His extensive editorial roles, including a founding Editor-inChief for Unmanned Systems and Associate Editor for Sciences China - Information Science, showcase his commitment to advancing scientific knowledge. He has played pivotal roles in various editorial boards, such as IET Book Series in Control and esteemed journals like IEEE Transactions on Automatic Control and Automatica.
Prof. Xie’s impact extends beyond editorial responsibilities; he has been a distinguished IEEE Distinguished Lecturer, a Board of Governors member for the IEEE Control System Society, and Vice President since January 2024. His leadership roles also include serving as General Chair of significant conferences, including the 62nd IEEE Conference on Decision and Control in December 2023.
His professional achievements, recognized by peers worldwide, include fellowships in the Academy of Engineering Singapore, the Institute of Electrical and Electronics Engineers (IEEE), International Federation of Automatic Control (IFAC), and the Chinese Automation Association (CAA).
In celebration of Prof. Xie’s 60th birthday, we invited 17 papers from friends and colleagues for this special issue. As editors, we extend our deepest gratitude to all the authors for their invaluable contributions. Special thanks to the Journal of Systems Science & Complexity editorial office, including Prof. Xiao-Shan Gao (Editor-in-Chief), Prof. Yanlong Zhao (Managing Editor), and Ms. Guoyun Wu (Editorial Director), for their steadfast support from the conception to the publication of this special issue.
On this momentous occasion, we express our profound appreciation for Prof. Lihua Xie for his unwavering commitment to advancing knowledge and look forward to the continued brilliance and innovation in the next chapters of his illustrious career.
Happy Birthday, Prof. Lihua Xie!

High-quality development focuses on total factor productivity. As a pioneer and demonstration zone of China's economic development, it is of great practical significance for national high-tech zones to effectively play a driving role in the region. Therefore, based on the data of Chinese prefecture-level cities from 2003--2018, the green total factor productivity was calculated as a measure of high-quality development. To alleviate the bias of the traditional DID model due to the neglect of policy spatial spillover, we constructed a spatial DID model to analyze the effect of the national high-tech zones on the green total factor productivity of local and neighboring cities. The results showed that the national high-tech zones increased the green total factor productivity. This policy had a clear promotion effect on cities without a national high-tech zone, and the effect was related to the original development level of the city and the spatial distance between cities; however, the cities with national high-tech zones has not shown a "strong team" or a "race to the top" spatial connection. In addition, the impact of national high-tech zones on regional green total factor productivity worked mainly through improving the regional innovation and entrepreneurship environment. Based on this, we suggest that the high-tech development highlands should to be created with national high-tech zones as the nodes, and the diversified regional cooperation system of national high-tech zones should to be built by breaking down the administrative boundaries.

In the context of the "double carbon" target, promoting the green and low-carbon transformation of economic and social development is a major systemic project. Developing renewable energy and improving energy efficiency will help to build a more efficient green energy system. Analyzing the effect of energy transformation has important reference value for formulating a reasonable carbon emission policy and achieving medium and long-term emission reduction targets. This study takes this as a starting point. Firstly, static panel and dynamic panel system generalized method of moments (GMM) are used to estimate the impact of energy transformation, renewable energy efficiency and non-renewable energy efficiency on major macroeconomic variables. Secondly, the intermediate production sector is subdivided into renewable energy production sector and non-renewable energy production sector. The dynamic stochastic general equilibrium (DSGE) model is constructed to analyze the short-term impact of energy transformation impact, renewable energy efficiency impact and non-renewable energy efficiency impact on major macroeconomic variables. The analysis shows that: 1) energy transformation promotes the transfer of resources between sectors, the output of renewable energy production sector will increase, while the output of non-renewable energy production sector and carbon emissions will decrease; 2) The improvement of two kinds of energy efficiency will produce economic expansion effect, but it will also produce energy rebound effect and increase carbon emissions; 3) At the end of the simulation period, the implementation of the carbon emission intensity policy will promote the growth effect of three shocks on output, but will also hinder the emission reduction effect and aggravate the rebound effect in the process of energy transformation. The implementation of the carbon tax policy will inhibit the rebound effect of two types of energy efficiency shocks on carbon emissions. In the process of energy transformation, we should rely on a reasonable carbon emission policy and formulate medium and long-term emission reduction targets. This study has important reference value for China to analyze the effect of energy transformation.

Bilevel Optimization recently became a very active research area. This is mainly due to its important applications from machine learning. In this paper, we give a gentle introduction to algorithms, theory, and applications of bilevel optimization. In particular, we will discuss the history of bilevel optimization, its applications in power grid, hyper-parameter optimization, meta learning, as well as algorithms for solving bilevel optimization and their convergence properties. We will mainly discuss algorithms for solving two types of bilevel optimization problems: lower-level problem is strongly convex and lower-level problem is convex. We will discuss gradient methods and value-function-based methods. Decentralized and federated bilevel optimization will also be discussed.

It is conjectured by Professor Zhi-Wei Sun that for each given odd prime $p>100, $ there always exists an solution $(x,y,z)\in[1,p]^3$ to the Pythagoras equation $x^2+y^2=z^2$ such that $x,y,z$ are quadratic residues or non-residues modulo $p$ respectively (eight cases in total). In this paper, we are able to prove the above assertion for all sufficiently large primes $p$, and the method is based on the recent Burgess bound for character sums of forms in many variables due to Lillian B. Pierce and Junyan Xu.

Climate damage and adaptation are key modules in the integrated assessment modelling on climate-economy system, both of which will directly affect the stringency of climate policy. This paper explores the recent academic advancement in these two aspects. As for climate damage, an emerging strand of literature not only focuses on the overall impact of rising temperature on economic production, but also sheds more spotlights on how rising temperature can cause economic losses through shocks to capital stock, labor supply, technical change and natural capital. Although recent studies illuminate directions for improving damage function under varying degrees of temperature rise, enormous disputes still hang over the concrete settings of the damage function. Existing climate-adaptation models commonly resort to either adaptive investment or market mechanisms. However, additional examinations should be warranted of related constraints on adaptation to avoid overestimating the effects of climate adaptation, as well as the regional heterogeneity in adaptation strategies. Future research should take into account social damages caused by climate change, other climate risk characteristics in addition to rising temperature, and how they channel corresponding effects. Furthermore, more empirical studies are needed to help refine the constructions and calibrations of the damage function, and probability tools are called for to investigate the uncertainties of climate damage and adaptation, in a shared effort to provide references for guiding climate policy practices.

Barrier option is a popular over-the-counter derivative in the Chinese market. Due to the discontinuity of its returns, financial institutions are mainly faced with the problem of delta value fluctuations in the process of dynamic hedging, resulting in higher hedging risks. We propose an efficient and stable willow tree method for barrier option pricing and greeks calculation for dynamic hedging of barrier options assuming the underlying asset price follows Merton's jump-diffusion model, which can also be extended to other stochastic processes. Compared with the existing methods, the willow tree method is more stable in calculating the delta, and the hedging cost is lower. An empirical analysis of the hedging effect of barrier options on the Shanghai Stock Exchange 50 Index is conducted from January 1, 2010 to September 30, 2021, and the model parameters are calibrated from the market data. The numerical results show that the willow tree method reduces hedging costs and hedging risks, and it can provide a new approach for domestic financial institutions to hedge barrier options and related structured products.

This paper proposes the real-time Realized EGARCH-MIDAS (RT-REGARCH-MIDAS) model which adequately captures the information content of high-frequency data, the current return information and the long memory of volatility to model and forecast Chinese stock market volatility. An empirical analysis based on the 5-minute high-frequency data of the Shanghai Stock Exchange Composite Index (SSEC) and the Shenzhen Stock Exchange Component Index (SZSEC) shows that the RT-REGARCH-MIDAS model outperforms a variety of competitor models in fitting the return data and can describe the stock market volatility better. Using robust loss functions and the model confidence set (MCS) test, the paper compares the out-of-sample forecasting ability of the model and other competitor models for Chinese stock market volatility. Our empirical results show that accounting for the information content of high-frequency data, the current return information and the long memory of volatility plays an important role in forecasting stock market volatility. As a consequence, the proposed RT-REGARCH-MIDAS model performs the best in forecasting Chinese stock market volatility. Further, according to the robustness checks, the superior volatility forecasting ability of the model is robust to alternative realized measure, alternative forecast windows, alternative MIDAS lags, alternative forecasting horizons and out-of-sample R² test. Finally, a volatility timing strategy shows that the proposed model yields more significant economic value of portfolio compared to the other models.

The real growth rate of gross domestic product (GDP) is an important indicator to measure the state of the economy. However, as it is released quarterly with a time lag, it fails to meet the timely economic analysis demand. In this paper, the mixed frequency dynamic factor model (MF-DFM) is used to nowcast quarterly GDP year-on-year growth rate based on timely large-scale monthly economic data, which improves the timeliness of economic analysis. In order to enhance the efficiency of utilizing a large number of available candidate economic variables and avoid the subjectivity of indicator selection in the factor model, this paper proposes a indicator selection method for MF-DFM with large-scale data, which uses the mean square prediction error of the binary dynamic single factor model as the basis for indicator selection. This method is applicable to selecting effective indicators amongst data with quarterly and monthly frequencies, missing values and jagged edges. The empirical analysis results indicate that compared with the traditional time series prediction models and the commonly used mixed frequency models, the MF-DFM based on screening variables by the binary model has a higher accuracy in predicting quarterly GDP growth rate, both for the stability period before COVID-19 and the recovery period after COVID-19. Moreover, the prediction for monthly GDP growth rate provided by this method has a high synchronization with the macroeconomic consistency index, which is conducive to improving the timeliness of economic analysis. This paper provides a new approach for real-time economic monitoring, prediction, and early warning based on indicator selection with the large-scale data.

Many practical problems in interdisciplinary sciences can be translated to the multivariable minimization problems of an energy function/functional in mathematics. There are two long-standing, critical problems in computational mathematics: finding the global minimum and finding the relationship between different minima. This paper mainly introduces the recently developed "solution landscape" concept and method. We will review the concept of solution landscape, saddle dynamics method for construction of solution landscape, and its applications on liquid crystals and quasicrystals.

It is of great theoretical and practical significance to accurately model and forecast the volatility of financial assets in the complex and changeable financial market environment. Therefore, based on a variety of volatility decomposition methods, and embedded with the Markov regime-switching approach, this study reconstructs multiple new heterogeneous autoregressive realized volatility models, and further takes Shanghai Stock Exchange 50ETF as the research object to compare the prediction accuracy of each model. The main empirical results are as follows. First, the model confidence set (MCS) test shows that the newly constructed model (MS-PHAR) combined with Markov regime-switching and quantile array volatility has the best predictive performance and various robustness checks confirm the above conclusion. Second, during the periods of high and low volatility, before and after the COVID-19 epidemic, and considering the leverage effect, the newly constructed MS-PHAR model still has a good performance.

Central bank communication is an important narrative text that receives a lot of attention from the market, and how to effectively extract key information from the high-dimensional text is a scientific problem to be studied in depth. In this paper, we apply the Sentiment Extraction via Screening and Topic Modeling method proposed by Ke et al. (2019) to measure central bank communication, which has the advantages of simplicity, transparency and replicability. Considering the characteristics of Chinese texts and the multi-instrument framework of China's monetary policy, we select the change values of several actual monetary policy interventions as supervised variables and then construct a central bank communication index, and forecast future actual monetary policy interventions based on generalized monetary policy rules. The results show that textual information on central bank communications helps to provide additional forecasting power. Compared with the indexes constructed by the existing literature based on text analysis methods such as keywords, supervised dictionaries and LDA methods, the index constructed in our paper has better forecasting power, especially with superior performance in long-term forecasting. We verify the effectiveness of central bank communication in guiding expectations from a predictive perspective, and provides feasible solutions for extracting textual information based on different target indicators.

In light of recent events such as "sweatshops", "environmental pollution", and "child slave labor", it has become imperative for enterprises to shift their focus from solely pursuing short-term economic gains to fulfilling social responsibilities in order to achieve sustainable and high-quality development. Focusing on how to coordinate the participation of brand firms and suppliers in fulfilling the social responsibilities of the upstream supply chain and taking account of the prosociality and reference effects of consumers, we use differential game method to build the decision-making model of decentralized decision-making, centralized decision-making, and unilateral cost-sharing contract between brand firms and suppliers. Furthermore, we design the bilateral cost- and revenue-sharing contract to achieve perfect coordination of the supply chain. The results suggest that: 1) An increase in the proportion of prosocial consumers will prompt brand firms and suppliers to improve their efforts of fulfilling social responsibility. However, excessive reliance on brand reputation to determine the level of social responsibility performance of the upstream supply chain may reduce the willingness of supply chain members to fulfill their social responsibilities. 2) The unilateral cost-sharing contract encourages suppliers to improve the efforts of fulfilling social responsibility, but do not affect the brand firm's efforts of fulfilling social responsibility. The contract stimulates the improvement of brand goodwill and consumer reference prices, thus promote the increase of profits for brand firms and suppliers. Moreover, when the reference effect for the level of social responsibility fulfillment of the upstream supply chain has a significant impact on the formation of consumers' reference price, the reference price under this contract will instead be higher than the centralized decision-making model. However, the contract is difficult to promote brand firms and suppliers to jointly improve social responsibility performance, and cannot maximize the total profit of the supply chain. 3) When the revenue sharing ratio of brand firms is at a moderate level, it can promote both brand firms and suppliers to participate in a bilateral cost- and revenue-sharing contract, and coordinate the supply chain perfectly.

To address the issues of information misrepresentation, false propaganda, and supervision difficulty in the e-commerce supply chain consisting of brands, MCNs, and e-commerce platforms under the background of “Live streaming+”, this paper first establishes a three-party game model based on evolutionary game theory, and then analyzes each player's game behavior and evolutionary stability strategy. Second, numerical simulation is used to investigate the influences of some key factors on the evolutionary trend of the dynamic system and its evolutionary stability strategy in terms of lower, medium, and higher return rate. It has been discovered that the standardized operation of the live streaming e-commerce supply chain is influenced by multiple factors. Under the lower return rate, no matter whether the e-commerce platforms supervise actively or not, brands and MCNs tend to operate in violation; under the medium and higher return rate, higher latent reputation loss, lower false propaganda factor, and lower trending channel effect would promote this system to evolve to the desired stable state. Finally, this paper puts forward constructive suggestions for the live streaming e-commerce supply chain to achieve standardized operations according to the practical operations.